Distance-based detection of cough, wheeze, and breath sounds on wearable devices

• Main Authors: Xue, Bing, Shi, Wen, Chotirmall, Sanjay Haresh, Koh, Vivian Ci Ai, Ang, Yi Yang, Tan, Rex Xiao, Ser, Wee
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Medicine; Acoustic Signal Processing; Distance Classification
• Online Access: https://hdl.handle.net/10356/161310
• Institution: Nanyang Technological University

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.